Multi-Objective Evolutionary Algorithm Based Engineering Desgin Optimization

1. A computer-implemented method of obtaining a set of diversified Pareto optimal solutions a multi-objective evolutionary algorithm (MOEA) based engineering design optimization of a product, said method comprising: receiving, in a computer system, a description of a product to be optimized; conducting a plurality of MOEA based engineering design optimizations of the product independently, the engineering design optimizations being configured to use a set of common design variables and a set of common design objective functions, wherein each of the engineering design optimizations differs from others in initial generation's design alternatives and evolution schemes consisted of Nondominated Sorting Genetic Algorithm (NSGA-II) and strength Pareto evolutionary algorithm (SPEA); obtaining a combined set of Pareto optimal solutions at one or more predefined checkpoints by combining Pareto optimal solutions resulted from said each of the engineering optimizations; and designating the combined set of Pareto optimal solutions as global Pareto optimal solutions when any or all of engineering design optimizations have converged based on spread and uniformity measurements, wherein the global Pareto optimal solutions are stored in a storage device and graphically displayed in a monitor upon user's instruction.

2. The computer-implemented method of claim 1 , further comprises randomly creating the initial generation's design alternatives for said each of the engineering design optimization.

3. The computer-implemented method of claim 1 , wherein the predefined one or more checkpoints are measured by numbers of generation.

4. The computer-implemented method of claim 1 , wherein the predefined one or more checkpoints are measured by numbers of objective function evaluation.

5. The computer-implemented method of claim 1 , wherein each of the combined set of Pareto optimal solutions is non-dominated to others.

6. A computer readable medium containing instructions for controlling a computer system for obtaining a set of diversified Pareto optimal solutions a multi-objective evolutionary algorithm (MOEA) based engineering design optimization of a product by a method comprising: receiving, in a computer system, a description of a product to be optimized; conducting a plurality of MOEA based engineering design optimizations of the product independently, the engineering design optimizations being configured to use a set of common design variables and a set of common design objective functions, wherein each of the engineering design optimizations differs from others in initial generation's design alternatives and evolution schemes consisted of Nondominated Sorting Genetic Algorithm (NSGA-II) and strength Pareto evolutionary algorithm (SPEA); obtaining a combined set of Pareto optimal solutions at one or more predefined checkpoints by combining Pareto optimal solutions resulted from said each of the engineering optimizations; and designating the combined set of Pareto optimal solutions as global Pareto optimal solutions when any or all of the engineering design optimizations has converged based on spread and uniformity measurements, wherein the global Pareto optimal solutions are stored in a storage device and graphically displayed in a monitor upon user's instruction.

7. The computer readable medium of claim 6 , further comprises randomly creating the initial generation's design alternatives for said each of the engineering design optimization.

8. A system for obtaining a set of diversified Pareto optimal solutions a multi-objective evolutionary algorithm (MOEA) based engineering design optimization of a product, said system comprising: a main memory for storing computer readable code for at least one application module; at least one processor coupled to the main memory, said at least one processor executing the computer readable code in the main memory to cause the at least one application module to perform operations by a method of: receiving a description of a product to be optimized; conducting a plurality of MOEA based engineering design optimizations of the product independently, the engineering design optimizations being configured to use a set of common design variables and a set of common design objective functions, wherein each of the engineering design optimizations differs from others in initial generation's design alternatives and evolution schemes consisted of Nondominated Sorting Genetic Algorithm (NSGA-II) and strength Pareto evolutionary algorithm (SPEA); obtaining a combined set of Pareto optimal solutions at one or more predefined checkpoints by combining Pareto optimal solutions resulted from said each of the engineering optimizations; and designating the combined set of Pareto optimal solutions as global Pareto optimal solutions when any or all of the engineering design optimizations has converged based on spread and uniformity measurements, wherein the global Pareto optimal solutions are stored in a storage device and graphically displayed in a monitor upon user's instruction.

9. The system of claim 8 , further comprises randomly creating the initial generation's design alternatives for said each of the engineering design optimization.